To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

Systems and methods for enhancing anomaly detection using a pattern dictionary are disclosed. An example method includes receiving, from a wearable device, physiological data of the user, and parsing the physiological data into a set of parsed phrases having a number of parsed phrases by applying a pattern dictionary encoder using a pattern dictionary. Each parsed phrase represents a respective subsequence of the physiological data. The example method includes determining a codelength corresponding to the physiological data based on the set of parsed phrases, and comparing (i) the number of parsed phrases to a parsed phrase threshold, and (ii) the codelength to a codelength threshold using an anomaly detection model. Responsive to the number of parsed phrases exceeding the parsed phrase threshold or the codelength exceeding the codelength threshold, the example method includes generating an alert for display on a user interface indicating that the physiological data is anomalous.

Systems and methods for enhancing anomaly detection using a pattern dictionary are disclosed. An example method includes receiving, from a wearable device, physiological data of the user, and parsing the physiological data into a set of parsed phrases having a number of parsed phrases by applying a pattern dictionary encoder using a pattern dictionary. Each parsed phrase represents a respective subsequence of the physiological data. The example method includes determining a codelength corresponding to the physiological data based on the set of parsed phrases, and comparing (i) the number of parsed phrases to a parsed phrase threshold, and (ii) the codelength to a codelength threshold using an anomaly detection model. Responsive to the number of parsed phrases exceeding the parsed phrase threshold or the codelength exceeding the codelength threshold, the example method includes generating an alert for display on a user interface indicating that the physiological data is anomalous.

Examples described herein generate training data for machine learning (ML) for natural language (NL) processing (such as semantic parsing for translating NL). A formula tree is generated based on sampling both a formula grammar and NL templates. Using the formula tree, an ML training data instance pair is generated comprising a formula example and an NL example. A context example may also be used during instantiation of the formula tree. An ML model is trained with training data including the ML training data instance pair, and ML output is generated from NL input. The ML output includes, for example, a machine-interpretable formula, a database querying language command, or a general programming language instruction. Some examples support context-free grammar, probabilistic context-free grammar, and/or non-context-free production rules.

Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Log files include log file content, some of which (especially a non-runtime portion) is in human-readable language. Translation of log file content is accomplished by: (i) generating first log content in a first human-readable language using a first resource bundle related to data translation; and (ii) translating the first log content to second log content, which corresponds to the first log content but is in a second human-readable language, using the first resource bundle. The translated log content may have annotations and/or processing rules applied to it. The translation of the present invention can help to keep the translation accurate and uniform so that the translated log content may be more effectively used in various ways.

A system and method configured for use in a text-to-speech (TTS) system is provided. Embodiments may include identifying, using one or more processors, a word or phrase as a named entity and identifying a language of origin associated with the named entity. Embodiments may further include transliterating the named entity to a script associated with the language of origin. If the TTS system is operating in the language of origin, embodiments may include passing the transliterated script to the TTS system. If the TTS system is not operating in the language of origin, embodiments may include generating a phoneme sequence in the language of origin using a grapheme to phoneme (G2P) converter.

A method, an apparatus, a device and a storage medium for outputting information are provided. The method includes: acquiring a to-be-translated sentence; determining a foreign language sentence matching the to-be-translated sentence from a preset set of sentence pairs, where the set of sentence pairs includes local language sentences and corresponding foreign language sentences; determining a target foreign language sentence of the to-be-translated sentence according to the to-be-translated sentence and the determined foreign language sentence; and outputting the target foreign language sentence.

A method, an apparatus, a device and a storage medium for outputting information are provided. The method includes: acquiring a to-be-translated sentence; determining a foreign language sentence matching the to-be-translated sentence from a preset set of sentence pairs, where the set of sentence pairs includes local language sentences and corresponding foreign language sentences; determining a target foreign language sentence of the to-be-translated sentence according to the to-be-translated sentence and the determined foreign language sentence; and outputting the target foreign language sentence.